Overview

Dataset statistics

Number of variables14
Number of observations1512
Missing cells272
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.3 KiB
Average record size in memory108.5 B

Variable types

Numeric8
Categorical6

Alerts

original_plataform has constant value "Netflix" Constant
titles has a high cardinality: 1512 distinct values High cardinality
genres has a high cardinality: 247 distinct values High cardinality
description has a high cardinality: 1471 distinct values High cardinality
stars has a high cardinality: 1461 distinct values High cardinality
level_0 is highly correlated with df_indexHigh correlation
df_index is highly correlated with level_0High correlation
number_of_votes is highly correlated with nueva_columnaHigh correlation
nueva_columna is highly correlated with number_of_votesHigh correlation
level_0 is highly correlated with df_indexHigh correlation
df_index is highly correlated with level_0High correlation
level_0 is highly correlated with df_indexHigh correlation
df_index is highly correlated with level_0High correlation
number_of_votes is highly correlated with nueva_columnaHigh correlation
nueva_columna is highly correlated with number_of_votesHigh correlation
original_plataform is highly correlated with typeHigh correlation
type is highly correlated with original_plataformHigh correlation
runtime has 240 (15.9%) missing values Missing
stars has 28 (1.9%) missing values Missing
level_0 is uniformly distributed Uniform
df_index is uniformly distributed Uniform
titles is uniformly distributed Uniform
stars is uniformly distributed Uniform
level_0 has unique values Unique
df_index has unique values Unique
titles has unique values Unique

Reproduction

Analysis started2022-03-05 12:26:33.509770
Analysis finished2022-03-05 12:26:43.178188
Duration9.67 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

level_0
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1512
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean757.9444444
Minimum0
Maximum1516
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2022-03-05T13:26:43.245860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile76.55
Q1379.75
median757.5
Q31136.25
95-th percentile1440.45
Maximum1516
Range1516
Interquartile range (IQR)756.5

Descriptive statistics

Standard deviation437.799282
Coefficient of variation (CV)0.5776139468
Kurtosis-1.199017016
Mean757.9444444
Median Absolute Deviation (MAD)378.5
Skewness0.001102879266
Sum1146012
Variance191668.2113
MonotonicityStrictly increasing
2022-03-05T13:26:43.332572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
10071
 
0.1%
10161
 
0.1%
10151
 
0.1%
10141
 
0.1%
10131
 
0.1%
10121
 
0.1%
10111
 
0.1%
10101
 
0.1%
10091
 
0.1%
Other values (1502)1502
99.3%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
15161
0.1%
15151
0.1%
15141
0.1%
15131
0.1%
15121
0.1%
15111
0.1%
15101
0.1%
15091
0.1%
15081
0.1%
15071
0.1%

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct1512
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean757.9444444
Minimum0
Maximum1516
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2022-03-05T13:26:43.427109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile76.55
Q1379.75
median757.5
Q31136.25
95-th percentile1440.45
Maximum1516
Range1516
Interquartile range (IQR)756.5

Descriptive statistics

Standard deviation437.799282
Coefficient of variation (CV)0.5776139468
Kurtosis-1.199017016
Mean757.9444444
Median Absolute Deviation (MAD)378.5
Skewness0.001102879266
Sum1146012
Variance191668.2113
MonotonicityStrictly increasing
2022-03-05T13:26:43.519862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
10071
 
0.1%
10161
 
0.1%
10151
 
0.1%
10141
 
0.1%
10131
 
0.1%
10121
 
0.1%
10111
 
0.1%
10101
 
0.1%
10091
 
0.1%
Other values (1502)1502
99.3%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
15161
0.1%
15151
0.1%
15141
0.1%
15131
0.1%
15121
0.1%
15111
0.1%
15101
0.1%
15091
0.1%
15081
0.1%
15071
0.1%

titles
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1512
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Harith Iskander: I Told You So
 
1
American Vandal
 
1
Laerte-se
 
1
The Eddy
 
1
Dolly Parton's Heartstrings
 
1
Other values (1507)
1507 

Length

Max length83
Median length17
Mean length19.81812169
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1512 ?
Unique (%)100.0%

Sample

1st rowZumbo's Just Desserts
2nd rowZona Rosa
3rd rowYoung Wallander
4th rowYou vs. Wild
5th rowYou

Common Values

ValueCountFrequency (%)
Harith Iskander: I Told You So1
 
0.1%
American Vandal1
 
0.1%
Laerte-se1
 
0.1%
The Eddy1
 
0.1%
Dolly Parton's Heartstrings1
 
0.1%
Franco Escamilla: Por la anécdota1
 
0.1%
The OA1
 
0.1%
The Open House1
 
0.1%
Rattlesnake1
 
0.1%
Terrorism Close Calls1
 
0.1%
Other values (1502)1502
99.3%

Length

2022-03-05T13:26:43.628785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the390
 
7.7%
of122
 
2.4%
a55
 
1.1%
in45
 
0.9%
to38
 
0.7%
37
 
0.7%
with29
 
0.6%
and28
 
0.5%
love25
 
0.5%
for23
 
0.5%
Other values (2553)4303
84.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

years
Real number (ℝ≥0)

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.143519
Minimum2001
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2022-03-05T13:26:43.714317image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2015
Q12017
median2018
Q32020
95-th percentile2020
Maximum2020
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.860071318
Coefficient of variation (CV)0.0009216744504
Kurtosis11.48221929
Mean2018.143519
Median Absolute Deviation (MAD)1
Skewness-2.215388351
Sum3051433
Variance3.459865309
MonotonicityNot monotonic
2022-03-05T13:26:43.782289image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2020386
25.5%
2019369
24.4%
2018315
20.8%
2017212
14.0%
2016122
 
8.1%
201555
 
3.6%
201422
 
1.5%
201315
 
1.0%
20126
 
0.4%
20114
 
0.3%
Other values (6)6
 
0.4%
ValueCountFrequency (%)
20011
 
0.1%
20031
 
0.1%
20041
 
0.1%
20071
 
0.1%
20081
 
0.1%
20091
 
0.1%
20114
 
0.3%
20126
 
0.4%
201315
1.0%
201422
1.5%
ValueCountFrequency (%)
2020386
25.5%
2019369
24.4%
2018315
20.8%
2017212
14.0%
2016122
 
8.1%
201555
 
3.6%
201422
 
1.5%
201315
 
1.0%
20126
 
0.4%
20114
 
0.3%

genres
Categorical

HIGH CARDINALITY

Distinct247
Distinct (%)16.3%
Missing1
Missing (%)0.1%
Memory size11.9 KiB
Comedy
316 
Documentary
126 
Drama
 
58
Reality-TV
 
50
Comedy, Drama
 
41
Other values (242)
920 

Length

Max length34
Median length15
Mean length15.38385175
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)7.3%

Sample

1st rowReality-TV
2nd rowComedy
3rd rowCrime, Drama, Mystery
4th rowAdventure, Reality-TV
5th rowCrime, Drama, Romance

Common Values

ValueCountFrequency (%)
Comedy316
20.9%
Documentary126
 
8.3%
Drama58
 
3.8%
Reality-TV50
 
3.3%
Comedy, Drama41
 
2.7%
Documentary, Crime40
 
2.6%
Documentary, Comedy31
 
2.1%
Animation, Action, Adventure27
 
1.8%
Comedy, Drama, Romance24
 
1.6%
Comedy, Romance24
 
1.6%
Other values (237)774
51.2%

Length

2022-03-05T13:26:43.880464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy617
21.0%
drama449
15.3%
documentary341
11.6%
crime187
 
6.4%
animation171
 
5.8%
action164
 
5.6%
adventure125
 
4.3%
thriller93
 
3.2%
short85
 
2.9%
romance83
 
2.8%
Other values (16)622
21.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

imdb
Real number (ℝ≥0)

Distinct66
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.686396815
Minimum2.4
Maximum9.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2022-03-05T13:26:43.964338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile4.7
Q16
median6.8
Q37.4
95-th percentile8.3
Maximum9.3
Range6.9
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.097880497
Coefficient of variation (CV)0.1641961324
Kurtosis0.3599492249
Mean6.686396815
Median Absolute Deviation (MAD)0.7
Skewness-0.5586127291
Sum10109.83198
Variance1.205341585
MonotonicityNot monotonic
2022-03-05T13:26:44.045700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.269
 
4.6%
6.868
 
4.5%
7.462
 
4.1%
7.360
 
4.0%
6.460
 
4.0%
6.559
 
3.9%
7.156
 
3.7%
6.351
 
3.4%
6.749
 
3.2%
6.647
 
3.1%
Other values (56)931
61.6%
ValueCountFrequency (%)
2.41
 
0.1%
2.52
0.1%
2.61
 
0.1%
2.91
 
0.1%
31
 
0.1%
3.22
0.1%
3.43
0.2%
3.52
0.1%
3.63
0.2%
3.71
 
0.1%
ValueCountFrequency (%)
9.31
 
0.1%
9.21
 
0.1%
9.11
 
0.1%
8.91
 
0.1%
8.86
 
0.4%
8.714
0.9%
8.611
0.7%
8.510
 
0.7%
8.424
1.6%
8.327
1.8%

runtime
Real number (ℝ≥0)

MISSING

Distinct182
Distinct (%)14.3%
Missing240
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean74.55424528
Minimum4
Maximum629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2022-03-05T13:26:44.135315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile23
Q141
median62
Q395
95-th percentile135.45
Maximum629
Range625
Interquartile range (IQR)54

Descriptive statistics

Standard deviation59.54394114
Coefficient of variation (CV)0.7986660037
Kurtosis26.00040214
Mean74.55424528
Median Absolute Deviation (MAD)28
Skewness4.1616253
Sum94833
Variance3545.480926
MonotonicityNot monotonic
2022-03-05T13:26:44.219301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3084
 
5.6%
6082
 
5.4%
4541
 
2.7%
5033
 
2.2%
4031
 
2.1%
2330
 
2.0%
2429
 
1.9%
2527
 
1.8%
9220
 
1.3%
9019
 
1.3%
Other values (172)876
57.9%
(Missing)240
 
15.9%
ValueCountFrequency (%)
41
 
0.1%
72
 
0.1%
101
 
0.1%
112
 
0.1%
123
 
0.2%
131
 
0.1%
141
 
0.1%
158
0.5%
163
 
0.2%
172
 
0.1%
ValueCountFrequency (%)
6291
0.1%
5731
0.1%
5721
0.1%
5421
0.1%
4941
0.1%
4911
0.1%
4521
0.1%
4361
0.1%
4032
0.1%
3931
0.1%

description
Categorical

HIGH CARDINALITY

Distinct1471
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Add a Plot
 
42
After his sudden firing, a popular radio DJ moves in with his aunt, bringing along his four spoiled children, and a plan to return to the airwaves.
 
1
Comedian Marc Maron riffs on topics including Donald Trump, a Rolling Stones concert, and the hat-buying experience.
 
1
In this unrestricted jaunt, comic Jim Norton offers a personal perspective on romance, desire, and sexual proclivities.
 
1
Mildred lives an ordinary until the day that Maud Spellbody crashes her broomstick into their balcony. Maud then introduces Mildred to Cackle's Academy - a school for young witches set high on a mountaintop.
 
1
Other values (1466)
1466 

Length

Max length421
Median length146
Mean length145.0489418
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1470 ?
Unique (%)97.2%

Sample

1st rowAmateur Australian chefs compete to impress patisserie chef Adriano Zumbo with their sweet creations. Those who don't fit the brief go head to head in the 'Zumbo test' to replicate his unique desserts.
2nd rowAdd a Plot
3rd rowFollow recently graduated police officer Kurt Wallander as he investigates his first case.
4th rowIn this interactive series, you'll make key decisions to help Bear Grylls survive, thrive and complete missions in the harshest environments on Earth.
5th rowA dangerously charming, intensely obsessive young man goes to extreme measures to insert himself into the lives of those he is transfixed by.

Common Values

ValueCountFrequency (%)
Add a Plot42
 
2.8%
After his sudden firing, a popular radio DJ moves in with his aunt, bringing along his four spoiled children, and a plan to return to the airwaves.1
 
0.1%
Comedian Marc Maron riffs on topics including Donald Trump, a Rolling Stones concert, and the hat-buying experience.1
 
0.1%
In this unrestricted jaunt, comic Jim Norton offers a personal perspective on romance, desire, and sexual proclivities.1
 
0.1%
Mildred lives an ordinary until the day that Maud Spellbody crashes her broomstick into their balcony. Maud then introduces Mildred to Cackle's Academy - a school for young witches set high on a mountaintop.1
 
0.1%
The Rescue Riders have been asked to find a precious golden dragon egg, and keep it safe from evil pirates.1
 
0.1%
Comedian Jerry Seinfeld performs at the Beacon Theatre in New York City with his take on everyday life, uncovering comedy in the commonplace.1
 
0.1%
Comedian Russell Howard brings his manic energy to a new stand-up special that tackles politics, childhood and why he's a jerk.1
 
0.1%
Women's rights attorney Gloria Allred takes on the biggest names in American culture as coverage of sexual assault allegations in the media become more prevalent.1
 
0.1%
Hugo Sanchez is tasked with leading the Cuervos into the Duel of the Birds tournament despite his personal life pulling him back toward the family business.1
 
0.1%
Other values (1461)1461
96.6%

Length

2022-03-05T13:26:44.322994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the1784
 
4.9%
a1595
 
4.4%
and1189
 
3.3%
of1097
 
3.0%
to950
 
2.6%
in816
 
2.2%
his481
 
1.3%
with365
 
1.0%
on345
 
0.9%
her299
 
0.8%
Other values (8046)27623
75.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

stars
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1461
Distinct (%)98.5%
Missing28
Missing (%)1.9%
Memory size11.9 KiB
Nat Faxon, Jay Gragnani, Ramone Hamilton, Sean Astin
 
3
John Schultz, Rose McIver, Ben Lamb, Alice Krige, Honor Kneafsey
 
2
Raúl Campos, Jan Suter, Sofia Niño de Rivera
 
2
Raúl Campos, Jan Suter, Carlos Ballarta
 
2
Seth Barrish, Mike Birbiglia
 
2
Other values (1456)
1473 

Length

Max length128
Median length61
Mean length58.68328841
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1439 ?
Unique (%)97.0%

Sample

1st rowGigi Falanga, Rachel Khoo, Adriano Zumbo
2nd rowRay Contreras, Pablo Morán, Manu Nna, Ana Julia Yeye
3rd rowAdam Pålsson, Leanne Best, Richard Dillane, Ellise Chappell
4th rowBear Grylls
5th rowPenn Badgley, Victoria Pedretti, Ambyr Childers, Elizabeth Lail

Common Values

ValueCountFrequency (%)
Nat Faxon, Jay Gragnani, Ramone Hamilton, Sean Astin3
 
0.2%
John Schultz, Rose McIver, Ben Lamb, Alice Krige, Honor Kneafsey2
 
0.1%
Raúl Campos, Jan Suter, Sofia Niño de Rivera2
 
0.1%
Raúl Campos, Jan Suter, Carlos Ballarta2
 
0.1%
Seth Barrish, Mike Birbiglia2
 
0.1%
Ulises Valencia, Franco Escamilla2
 
0.1%
Bill D'Elia, Chris D'Elia2
 
0.1%
Gigi Saul Guerrero2
 
0.1%
Marcus Raboy, Vir Das2
 
0.1%
Shannon Hartman, Jo Koy2
 
0.1%
Other values (1451)1463
96.8%
(Missing)28
 
1.9%

Length

2022-03-05T13:26:44.559480image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael79
 
0.6%
david77
 
0.6%
john72
 
0.6%
paul50
 
0.4%
james38
 
0.3%
jay36
 
0.3%
mike36
 
0.3%
alex33
 
0.3%
tom33
 
0.3%
chris32
 
0.3%
Other values (6380)11788
96.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_of_votes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1333
Distinct (%)88.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13386.62078
Minimum5
Maximum785704
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2022-03-05T13:26:44.654550image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile64
Q1561.5
median2024
Q37642.5
95-th percentile62663.5
Maximum785704
Range785699
Interquartile range (IQR)7081

Descriptive statistics

Standard deviation41975.79153
Coefficient of variation (CV)3.135652545
Kurtosis104.8427738
Mean13386.62078
Median Absolute Deviation (MAD)1792
Skewness8.383597886
Sum20227184
Variance1761967074
MonotonicityNot monotonic
2022-03-05T13:26:44.741781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
956
 
0.4%
645
 
0.3%
684
 
0.3%
224
 
0.3%
204
 
0.3%
434
 
0.3%
914
 
0.3%
3684
 
0.3%
2923
 
0.2%
18523
 
0.2%
Other values (1323)1470
97.2%
ValueCountFrequency (%)
51
0.1%
71
0.1%
81
0.1%
91
0.1%
102
0.1%
111
0.1%
121
0.1%
161
0.1%
172
0.1%
181
0.1%
ValueCountFrequency (%)
7857041
0.1%
4597121
0.1%
4265561
0.1%
3563681
0.1%
3459961
0.1%
3123011
0.1%
2802801
0.1%
2791201
0.1%
2758501
0.1%
2730621
0.1%

type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
TV Show
1008 
Movie
504 

Length

Max length7
Median length7
Mean length6.333333333
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTV Show
2nd rowTV Show
3rd rowTV Show
4th rowTV Show
5th rowTV Show

Common Values

ValueCountFrequency (%)
TV Show1008
66.7%
Movie504
33.3%

Length

2022-03-05T13:26:44.839057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T13:26:44.893026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
tv1008
40.0%
show1008
40.0%
movie504
20.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

original_plataform
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Netflix
1512 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNetflix
2nd rowNetflix
3rd rowNetflix
4th rowNetflix
5th rowNetflix

Common Values

ValueCountFrequency (%)
Netflix1512
100.0%

Length

2022-03-05T13:26:44.948026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-05T13:26:44.996069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
netflix1512
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

nueva_columna
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1499
Distinct (%)99.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.02037698931
Minimum1.120014662 × 10-5
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2022-03-05T13:26:45.046372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.120014662 × 10-5
5-th percentile0.0001200386199
Q10.000842121502
median0.003363518758
Q30.01163067093
95-th percentile0.09676732482
Maximum1.4
Range1.3999888
Interquartile range (IQR)0.01078854942

Descriptive statistics

Standard deviation0.06851416496
Coefficient of variation (CV)3.362330123
Kurtosis145.4942344
Mean0.02037698931
Median Absolute Deviation (MAD)0.003030426796
Skewness9.888435633
Sum30.78963084
Variance0.004694190801
MonotonicityNot monotonic
2022-03-05T13:26:45.128371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.070652173912
 
0.1%
0.020543806652
 
0.1%
0.0018181818182
 
0.1%
0.047058823532
 
0.1%
0.011764705882
 
0.1%
0.0014222563912
 
0.1%
0.093752
 
0.1%
0.034146341462
 
0.1%
0.0031578947372
 
0.1%
0.0049339819322
 
0.1%
Other values (1489)1491
98.6%
ValueCountFrequency (%)
1.120014662 × 10-51
0.1%
1.892489211 × 10-51
0.1%
2.0630351 × 10-51
0.1%
2.413235756 × 10-51
0.1%
2.422978648 × 10-51
0.1%
2.529610856 × 10-51
0.1%
2.543382004 × 10-51
0.1%
2.925645783 × 10-51
0.1%
2.936378467 × 10-51
0.1%
3.009458298 × 10-51
0.1%
ValueCountFrequency (%)
1.41
0.1%
0.871
0.1%
0.76251
0.1%
0.561
0.1%
0.55555555561
0.1%
0.55454545451
0.1%
0.51428571431
0.1%
0.48333333331
0.1%
0.406251
0.1%
0.3851
0.1%

nueva_columna2
Real number (ℝ≥0)

Distinct1499
Distinct (%)99.2%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.02037698931
Minimum1.120014662 × 10-5
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.9 KiB
2022-03-05T13:26:45.218816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.120014662 × 10-5
5-th percentile0.0001200386199
Q10.000842121502
median0.003363518758
Q30.01163067093
95-th percentile0.09676732482
Maximum1.4
Range1.3999888
Interquartile range (IQR)0.01078854942

Descriptive statistics

Standard deviation0.06851416496
Coefficient of variation (CV)3.362330123
Kurtosis145.4942344
Mean0.02037698931
Median Absolute Deviation (MAD)0.003030426796
Skewness9.888435633
Sum30.78963084
Variance0.004694190801
MonotonicityNot monotonic
2022-03-05T13:26:45.303056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.070652173912
 
0.1%
0.0018181818182
 
0.1%
0.0014222563912
 
0.1%
0.12
 
0.1%
0.0031578947372
 
0.1%
0.0049339819322
 
0.1%
0.074725274732
 
0.1%
0.034146341462
 
0.1%
0.020543806652
 
0.1%
0.047058823532
 
0.1%
Other values (1489)1491
98.6%
ValueCountFrequency (%)
1.120014662 × 10-51
0.1%
1.892489211 × 10-51
0.1%
2.0630351 × 10-51
0.1%
2.413235756 × 10-51
0.1%
2.422978648 × 10-51
0.1%
2.529610856 × 10-51
0.1%
2.543382004 × 10-51
0.1%
2.925645783 × 10-51
0.1%
2.936378467 × 10-51
0.1%
3.009458298 × 10-51
0.1%
ValueCountFrequency (%)
1.41
0.1%
0.871
0.1%
0.76251
0.1%
0.561
0.1%
0.55555555561
0.1%
0.55454545451
0.1%
0.51428571431
0.1%
0.48333333331
0.1%
0.406251
0.1%
0.3851
0.1%

Interactions

2022-03-05T13:26:41.563109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:34.418161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:35.449257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:36.675265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:37.918196image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.033507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.873259image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.737835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.671355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:34.557173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:35.579479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:36.807931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:38.039181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.129734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.977019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.840470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.776010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:34.682564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:35.771952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:36.946100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:38.340220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.237524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.083388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.938432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.876187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:34.816987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:35.923009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:37.088934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:38.456192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.335524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.193963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.042206image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.974195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:34.942777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:36.058501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:37.252774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:38.573824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.431739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.300928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.142080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:42.208149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:35.059225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:36.192957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:37.445154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:38.710009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.539403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.403290image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.242699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:42.317291image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:35.196161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:36.378509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:37.632537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:38.833402image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.671560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.522617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.348973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:42.424224image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:35.323962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:36.545791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:37.789180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:38.934906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:39.773730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:40.629501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-03-05T13:26:41.453586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-03-05T13:26:45.383047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-03-05T13:26:45.497131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-03-05T13:26:45.603115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-03-05T13:26:45.706109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-03-05T13:26:42.597693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-03-05T13:26:42.803846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-05T13:26:42.957618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-05T13:26:43.069845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

level_0df_indextitlesyearsgenresimdbruntimedescriptionstarsnumber_of_votestypeoriginal_plataformnueva_columnanueva_columna2
000Zumbo's Just Desserts2016Reality-TV6.952Amateur Australian chefs compete to impress patisserie chef Adriano Zumbo with their sweet creations. Those who don't fit the brief go head to head in the 'Zumbo test' to replicate his unique desserts.Gigi Falanga, Rachel Khoo, Adriano Zumbo1779TV ShowNetflix0.0038790.003879
111Zona Rosa2019Comedy6.0<NA>Add a PlotRay Contreras, Pablo Morán, Manu Nna, Ana Julia Yeye33TV ShowNetflix0.1818180.181818
222Young Wallander2020Crime, Drama, Mystery6.7<NA>Follow recently graduated police officer Kurt Wallander as he investigates his first case.Adam Pålsson, Leanne Best, Richard Dillane, Ellise Chappell5419TV ShowNetflix0.0012360.001236
333You vs. Wild2019Adventure, Reality-TV6.720In this interactive series, you'll make key decisions to help Bear Grylls survive, thrive and complete missions in the harshest environments on Earth.Bear Grylls1977TV ShowNetflix0.0033890.003389
444You2018Crime, Drama, Romance7.845A dangerously charming, intensely obsessive young man goes to extreme measures to insert himself into the lives of those he is transfixed by.Penn Badgley, Victoria Pedretti, Ambyr Childers, Elizabeth Lail134932TV ShowNetflix0.0000580.000058
555YooHoo to the Rescue2019Family6.9<NA>In a series of magical missions, quick-witted YooHoo and his can-do crew travel the globe to help animals in need.Ryan Bartley, Kira Buckland, Lucien Dodge, Kyle Hebert37TV ShowNetflix0.1864860.186486
666Yankee2019Drama6.040On the run from the police, an Arizona man crosses into Mexico and gets deeply involved in drug trafficking, with the help of modern technology.Pablo Lyle, Ana Layevska, Pamela Almanza, Sebastián Ferrat458TV ShowNetflix0.01310.0131
777Wu Assassins2019Action, Crime, Drama6.444A warrior chosen as the latest and last Wu Assassin must search for the powers of an ancient triad and restore balance in San Francisco's Chinatown.Iko Uwais, Byron Mann, Li Jun Li, Lawrence Kao9336TV ShowNetflix0.0006860.000686
888World's Most Wanted2020Documentary, Crime7.1<NA>Heinous criminals have avoided capture despite massive rewards and global investigations. This docuseries profiles five of the world's most wanted.Jennifer Julian, Thomas Fuentes, Calogero Germaná, David Lorino1495TV ShowNetflix0.0047490.004749
999World of Winx2016Animation, Action, Comedy6.830The Winx travel all over the world searching for talent for WOW. and preventing the mysterious talent thief from kidnapping them.Rebecca Soler, Alysha Deslorieux, Haven Paschall, Eileen Stevens556TV ShowNetflix0.012230.01223

Last rows

level_0df_indextitlesyearsgenresimdbruntimedescriptionstarsnumber_of_votestypeoriginal_plataformnueva_columnanueva_columna2
150215071507Home: For the Holidays2017Animation, Short, Adventure4.845Oh takes it upon himself to introduce Christmas joy to his fellow Boovs. Unfortunately, his well-meaning mission nearly destroys the city.Blake Lemons, Kelly Clarkson, Ryan Crego, Rachel Crow, Kelly Donohue104TV ShowNetflix0.0461540.046154
150315081508Frankenstein's Monster's Monster, Frankenstein2019Short, Comedy5.932David Harbour delves into the enigmatic history of his legendary acting family, as he examines his father's legacy and role in a made-for-TV play.Daniel Gray Longino, David Harbour, Kate Berlant, Alex Ozerov, Mary Woronov1870TV ShowNetflix0.0031550.003155
150415091509Captain Underpants: Epic Choice-o-rama2020Animation, Short, Action6.0<NA>Add a PlotTodd Grimes, Nat Faxon, Jay Gragnani, Ramone Hamilton, Sean Astin64TV ShowNetflix0.093750.09375
150515101510A StoryBots Christmas2017Short, Family, Fantasy6.124When Bo mistakenly thinks that her friends don't like her gifts, she heads to the North Pole to ask Santa for help making better presents. She learns along the way that Christmas is about far more than just the toys.Jeff Gill, Evan Spiridellis, Judy Greer, Erin Fitzgerald, Fred Tatasciore, Jeff Gill121TV ShowNetflix0.0504130.050413
150615111511A Family Reunion Christmas2019Short, Comedy, Family5.628The McKellans are back to spread Christmas joy in this holiday special about the importance of family, forgiveness, and empathy.Robbie Countryman, Tia Mowry-Hardrict, Anthony Alabi, Talia Jackson, Isaiah Russell-Bailey119TV ShowNetflix0.0470590.047059
150715121512Ralphie May: Unruly2015Comedy4.783Filmed in front of a raucous crowd, comedian Ralphie May unleashes his hilariously raunchy perspective in his first Netflix original stand-up special.John Asher, Ralphie May357MovieNetflix0.0131650.013165
150815131513John Hodgman: Ragnarok2013Comedy, Music6.268The deranged millionaire John Hodgman plays his last Ragnarok stand-up comedy show on the last day of human history: December 21, 2012.Lance Bangs, John Hodgman, Scott Adsit, Cynthia J. Hopkins, Joel Ronson292MovieNetflix0.0212330.021233
150915141514Jimmy Carr: Funny Business2016Comedy7.262A man, with an incredibly stupid laugh, tells jokes to an audience.Sam Wrench, Jimmy Carr3445MovieNetflix0.002090.00209
151015151515Anthony Jeselnik: Thoughts and Prayers2015Comedy7.859Stand up comedian and former Late Night with Jimmy Fallon writer Anthony Jeselnik brings his dark humor and wit to San Francisco.Adam Dubin, Anthony Jeselnik, Peggy4300MovieNetflix0.0018140.001814
15111516151613th: A Conversation with Oprah Winfrey & Ava DuVernay2017Documentary, Short7.037Oprah Winfrey sits down with other to discuss social and cultural issues.Oprah Winfrey, Ava DuVernay174MovieNetflix0.040230.04023